SimpleViT misleading summary

[asusdisciple]

First of all: Thanks for the awesome implementations of various Vision Transoformer models!
It really helped a lot.

I personally had an issue with the summary of the SimpleViT, which implies that this implementation comes with MixUp and RandAugment. However this transforms are not in the code itself, maybe it should be noted that they have to be implemented by the user or you should leave away the paragraph right away to make it more clear and concise (since transforms dont have anything to do with the model structure anyways).
Best regards

[tjyuyao]

In the paper Better plain ViT baselines for ImageNet-1k, it says:

Notably absent from this baseline are further architectural changes, regularizers such as dropout or stochastic depth [7], ...

Doesn't this means it does NOT change the dropout settings? In the README.md of current project, is says:

Among these simplifications include 2d sinusoidal positional embedding, global average pooling (no CLS token), no dropout, batch sizes of 1024 rather than 4096, and use of RandAugment and MixUp augmentations.

It seems to be a contradiction.